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一种基于TOF相机的抓取机器人手眼标定新方法。

A novel hand-eye calibration method of picking robot based on TOF camera.

作者信息

Zhang Xiangsheng, Yao Meng, Cheng Qi, Liang Gunan, Fan Feng

机构信息

Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, China.

Visual Algorithm R&D Department, XINJIE Electronic Limited Company, Wuxi, Jiangsu, China.

出版信息

Front Plant Sci. 2023 Jan 17;13:1099033. doi: 10.3389/fpls.2022.1099033. eCollection 2022.

Abstract

Aiming at the stability of hand-eye calibration in fruit picking scene, a simple hand-eye calibration method for picking robot based on optimization combined with TOF (Time of Flight) camera is proposed. This method needs to fix the TOF depth camera at actual and calculated coordinates of the peach the end of the robot, operate the robot to take pictures of the calibration board from different poses, and record the current photographing poses to ensure that each group of pictures is clear and complete, so as to use the TOF depth camera to image the calibration board. Obtain multiple sets of calibration board depth maps and corresponding point cloud data, that is, "eye" data. Through the circle center extraction and positioning algorithm, the circle center points on each group of calibration plates are extracted, and a circle center sorting method based on the vector angle and the center of mass coordinates is designed to solve the circle center caused by factors such as mirror distortion, uneven illumination and different photographing poses. And through the tool center point of the actuator, the coordinate value of the circle center point on the four corners of each group of calibration plates in the robot end coordinate system is located in turn, and the "hand" data is obtained. Combined with the SVD method, And according to the obtained point residuals, the weight coefficients of the marker points are redistributed, and the hand-eye parameters are iteratively optimized, which improves the accuracy and stability of the hand-eye calibration. the method proposed in this paper has a better ability to locate the gross error under the environment of large gross errors. In order to verify the feasibility of the hand-eye calibration method, the indoor picking experiment was simulated, and the peaches were identified and positioned by combining deep learning and 3D vision to verify the proposed hand-eye calibration method. The JAKA six-axis robot and TuYang depth camera are used to build the experimental platform. The experimental results show that the method is simple to operate, has good stability, and the calibration plate is easy to manufacture and low in cost. work accuracy requirements.

摘要

针对水果采摘场景下手眼标定的稳定性问题,提出了一种基于优化并结合TOF(飞行时间)相机的采摘机器人手眼标定方法。该方法需将TOF深度相机固定在机器人末端桃子的实际坐标和计算坐标处,操作机器人从不同姿态拍摄标定板,并记录当前拍摄姿态,以确保每组图片清晰完整,进而利用TOF深度相机对标定板成像。获取多组标定板深度图及相应的点云数据,即“眼”数据。通过圆心提取与定位算法,提取每组标定板上的圆心点,并设计一种基于向量夹角和质心坐标的圆心排序方法,以解决由镜面畸变、光照不均和拍摄姿态不同等因素导致的圆心问题。并通过执行器的工具中心点,依次定位每组标定板四个角上圆心点在机器人末端坐标系中的坐标值,得到“手”数据。结合奇异值分解(SVD)方法,根据得到的点残差重新分配标记点的权重系数,对手眼参数进行迭代优化,提高了手眼标定的精度和稳定性。本文提出的方法在大误差环境下具有较好的粗大误差定位能力。为验证手眼标定方法的可行性,模拟了室内采摘实验,通过结合深度学习和3D视觉对桃子进行识别与定位,以验证所提出的手眼标定方法。利用JAKA六轴机器人和图漾深度相机搭建实验平台。实验结果表明,该方法操作简单、稳定性好,标定板易于制作且成本低,满足工作精度要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57ba/9888730/dea21884be02/fpls-13-1099033-g001.jpg

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